Today’s organisations are no strangers to the Internet of Things (IoT) and are increasingly adopting it to support their day-to-day tasks. In Asia Pacific alone, the IoT market is expected to grow to US$95.7 billion by 2022 with a compound annual growth rate of 11.3 percent.
In fact, manufacturers, particularly, are recognising the need to embrace industrial IoT (IIoT) to transform their business and stay ahead of the competition. IIoT rollouts are moving from trials to full deployments, and most manufacturers are planning to scale up the use of IoT in their operations. According to a recent Osborne Clarke report, 63 percent of manufacturing, manufacturing services, logistics and supply chain related companies in Asia have already implemented and are looking to expand the use of IIoT in their business.
Optimising operations is a strong motivating factor to do so. As per McKinsey’s research, IIoT has the potential to capture productivity gains worth US$216 billion to US$627 billion. By embedding sensors into machines and systems in their factories, manufacturers can gain a holistic view of the full production process in real time. They can also use the IIoT sensor data to quickly identify bottlenecks and make adjustments to reduce waste and improve operational efficiencies.
With connected inventory systems, for example, manufacturers can monitor their inventory in real time to minimise the risk of supply disruptions. They can also predict and plan for future inventory needs more accurately based on the historical data from those IIoT systems. With these capabilities, manufacturers will be able to realise a demand-driven inventory planning and potentially reduce their inventory costs by 20 to 50 percent.
IIoT can also help manufacturers achieve zero downtime by empowering them to perform the right maintenance routines. For asset-heavy industries, unplanned equipment outages can result in big losses in revenues and productivity. Some of the leading automotive manufacturers estimate that every minute of unplanned downtime could cost them as much as US$15,000 to US$20,000, and that a single downtime event could cost approximately US$2 million.
Preventing costly equipment downtimes require manufacturers to process and analyse time-series (or real-time) sensor data from their IIoT systems. By doing so, they will be able to identify warning signs of potential problems (such as detecting signs of mechanical wear and degradation before they become apparent), predict when an equipment requires maintenance, and get it serviced before it causes downtime.
Driving growth for manufacturers with IIoT
An IIoT initiative relies heavily on the manufacturer’s ability to process and analyse the wealth of data it provides. Manufacturers in Asia Pacific that are looking to leverage IIoT will therefore need to overcome the following hurdles:
- The immense volumes and variety of IIoT data
Data streaming from IIoT systems can generate petabytes of data. Since those data will come in diverse formats, standards, and protocols, it can be challenging for manufacturers to ingest it. - A wide range of analytical and predictive modelling capabilities needed
Predictive modelling capabilities are crucial to delivering insights. However, they require diverse analytical options (including machine learning), which may not be offered by existing big data platforms. - The challenge of analysing streaming data in real time
Generating value from IoT entails effectively managing both data at rest as well as data in motion. In fact, the success of IIoT deployments depends on the manufacturer’s ability to gain insights out of all this fast-moving, high-volume data. For instance, continuous monitoring and predictive maintenance require manufacturers to be able to effectively ingest, store, and process the data streaming in from sensors in real time or near-real time in order to instantly deliver insights and action. Despite the importance of this capability, a recent report by AOPG Insights and Cloudera found that 82 percent of ASEAN organisations are not processing data in motion. Respondents cited security and complexity of data as two of the top 3 obstacles to implementing real-time analytics.
A scalable, real-time, end-to-end streaming data platform — which ingests, curates, and analyses data to deliver key actionable insights — can help manufacturers overcome the complexities of IIoT. It does so by enabling manufacturers to manage, control, and monitor the edge for IIoT initiatives; and adopt a no-code approach to create visual flows for building complex data ingestion or transformation with drag-and-drop ease. Additionally, it helps manufacturers to track data provenance and lineage of streaming data; as well as manage and process multiple streams of real-time data at high volume using advanced techniques, to generate key insights for predictive analytics.
The benefits of having an end-to-end streaming data platform are exemplified in the case of Zoomlion, a Chinese manufacturer of construction machinery and sanitation equipment. The platform allows the company to ingest, store and process data from its connected machines, internal core business systems, and third-party sources. By continuously analysing equipment operations, detecting potential failures, and providing fault warnings and operational statistics, Zoomlion savings of 30 percent on reduced its manpower and maintenance costs by 30 percent. Insights from analysing IIoT data also allowed the company to offer new services, which led to a 30 percent increase in value-added service revenue.
Manufacturers in Asia Pacific are increasingly adopting IIoT to enhance their operational efficiency and enjoy cost savings. As the value of IIoT lies in the data they generate, manufacturers will need to be able to effectively manage and analyse the massive amount and variety of sensor data to fully benefit from IIoT.
